STAT5003

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# .text-shadow[.white[STAT5003]]

## .white[Week 1: Basics of R programming]
## .white[& static graphics]

### .white[Dr. Justin Wishart]

### .white[Semester 2, 2020]

---

## Aims of this course <i class="fas  fa-university fa-pull-right "></i>

* Learn .brand-red[concepts] and .brand-red[methods] related to statistical data analysis and statistical learning

* Apply the methods on .brand-red[real datasets] in <svg style="height:100;fill:steelblue;" viewBox="0 0 581 512"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg> (the statistical computing language)

* Aim is to teach you to use the methods, not necessarily to go into all the mathematical details

---

## Basics of Statistical Computing

* Review basic statistical concepts
* Introduction to <a href="https://www.r-project.org/"><svg style="height:50;fill:steelblue;" viewBox="0 0 581 512"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg></a>
* Reproducible coding using Rmarkdown <img src=https://github.com/rstudio/hex-stickers/blob/master/PNG/rmarkdown.png?raw=true width="50px" />
* Recommended IDE via <a href="https://rstudio.com/products/rstudio/"><img src=https://rstudio.com/wp-content/uploads/2018/10/RStudio-Logo-flat.svg width="100px" /></a>

---

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# Review of basic statistical concepts

---

## Population

* __Definition__:
    - The set of data (numeric or otherwise) corresponding to the entire collection of units about which information is sought.
* __Examples__:
    - Blood pressure – Blood pressure readings of ALL people in Australia.
    - The number of languages spoken from ALL currently enrolled students in University of Sydney

---

## Sample

* __Definition:__
    - A subset of the population data that are actually collected in the course of a study.
* __Examples__:
    - Blood pressure readings of 1000 randomly selected people in Australia.
    - The number of languages spoken from 500 randomly selected students currently enrolled in University of Sydney.
    
> In most studies, it is difficult to obtain information about the whole population. That is why we rely on samples to make estimates and inferences related to the whole population.

---

## Parameters vs statistics

* A .brand-red[parameter] is a number that describes a population.
    - Notation usually denoted with Greek letters. e.g. `$\mu$`, `$\sigma$`
* A .brand-red[statistic] is a number that describes a sample.
    - Sample statistics are usually denoted using Roman letters, e.g. `$x$`, `$s$`.
* A parameter is a fixed number (usually unknown). A statistic is a variable whose value varies from sample to sample.

---

## Descriptive statistics – numeric and graphics

Many methods are available for summarising data in both **numeric** and **graphical** form

Numeric measures:

* Measure of location
    – Mean, Median, Mode for numeric data
    - Counts, proportions for categorical data
* Measure of spread
    – Standard deviation, MAD (median absolute deviation), IQR
* Others:
    – Min, Max, Quartile, Five number summaries (used later in boxplot)

---

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# Basic statistical graphics <svg style="height:0.8em;top:.04em;position:relative;" viewBox="0 0 581 512"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg> and <img src = https://github.com/rstudio/hex-stickers/blob/master/PNG/ggplot2.png?raw=true width = 50px/>

---

## Types of graphics covered in this course

* Become familiar with simple graphics using base <svg style="height:0.8em;top:.04em;position:relative;fill:steelblue;" viewBox="0 0 581 512"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg> and ggplot graphics <img src = https://github.com/rstudio/hex-stickers/blob/master/PNG/ggplot2.png?raw=true width = 50px/>
* base <svg style="height:0.8em;top:.04em;position:relative;fill:steelblue;" viewBox="0 0 581 512"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg> use the built in plotting functions
    - typically good for quick plots of simple datasets
* ggplot graphics <img src = https://github.com/rstudio/hex-stickers/blob/master/PNG/ggplot2.png?raw=true width = 50px/> 
    - Name meaning the grammar of graphics
    - Typically better for more complicated datasets
* Not covered but honorable mention <img src=https://www.vectorlogo.zone/logos/plot_ly/plot_ly-ar21.svg height=50px />
    - Plotly is a powerful plotting library
    - Can do interactive graphics
    
---

## Simple example dataframe for plots

```r
example.dat <- data.frame(x = rnorm(100),
                          y = runif(100),
                          cat = sample(LETTERS[1:2], prob = c(1, 3), size = 100, replace = TRUE))
head(example.dat)
```

```
##             x          y cat
## 1 -1.01720533 0.53678627   B
## 2  0.66728073 0.90411240   B
## 3 -0.34239565 0.10107771   B
## 4 -0.80917484 0.03485421   A
## 5 -0.17392371 0.08812804   B
## 6  0.01421694 0.35850014   B
```

---

## Single numeric variable: Boxplot in base <svg style="height:0.8em;top:.04em;position:relative;fill:steelblue;" viewBox="0 0 581 512"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg>

```r
boxplot(example.dat$x)
```

---

## Single numeric variable: Boxplot in <img src = https://github.com/rstudio/hex-stickers/blob/master/PNG/ggplot2.png?raw=true width = 50px/>

```r
library(ggplot2) # Only need to load the library once in an R session
ggplot(example.dat, aes(y = x)) + geom_boxplot() + theme_minimal()
```

---

## Single numeric variable: Histogram in base <svg style="height:0.8em;top:.04em;position:relative;fill:steelblue;" viewBox="0 0 581 512"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg>

```r
hist(example.dat$x)
```

---

## Single numeric variable: Histogram in <img src = https://github.com/rstudio/hex-stickers/blob/master/PNG/ggplot2.png?raw=true width = 50px/>

```r
ggplot(example.dat, aes(x = x)) + geom_histogram() + theme_minimal()
```

---

## Single categorical variable: Bar plot in base <svg style="height:0.8em;top:.04em;position:relative;fill:steelblue;" viewBox="0 0 581 512"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg>

```r
barplot(table(example.dat$cat))
```

---

## Single categorical variable: Bar plot <img src = https://github.com/rstudio/hex-stickers/blob/master/PNG/ggplot2.png?raw=true width = 50px/>

```r
ggplot(example.dat, aes(x = cat)) + geom_bar() + theme_bw() # Change the theme
```

---

## Two numeric variables: Scatterplot in base <svg style="height:0.8em;top:.04em;position:relative;fill:steelblue;" viewBox="0 0 581 512"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg>

```r
# These two plot commands are near equivalent
plot(y ~ x, data = example.dat, main = "formula input")
plot(example.dat$x, example.dat$y, main = "argument input")
```

---

## Two numeric variables: Scatterplot in <img src = https://github.com/rstudio/hex-stickers/blob/master/PNG/ggplot2.png?raw=true width = 50px/>

```r
ggplot(example.dat, aes(x = x, y = y)) + geom_point() # default theme here
```

---

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background-image: url("https://raw.githubusercontent.com/garthtarr/sydney_xaringan/master/assets/USydLogo-white.svg")
background-size: 20%
background-position: 95% 95%

# A crash course in <svg style="height:100;fill:white;" viewBox="0 0 581 512"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg>

---

## What is <svg style="height:50;fill:steelblue;" viewBox="0 0 581 512"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg>?

* Free, open source software designed for statistical computing
* Runs on Windows, Mac, Linux and other flavours of Unix
* Provides an interactive environment, but it is also an interpreted
programming language
* Its power lies in the thousands of contributed packages on

---

## Base <svg style="height:0.8em;top:.04em;position:relative;fill:steelblue;" viewBox="0 0 581 512"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg> and the <img src="assets/tidyverse.png" width=50px/>

* The `tidyverse` popularised by Hadley Wickham and the team at <a href="https://rstudio.com/products/rstudio/"><img src=https://rstudio.com/wp-content/uploads/2018/10/RStudio-Logo-flat.svg width="100px" /></a>
    - Has a (somewhat) standardised syntax (pipes `%>%` are king except for `+` in `ggplot2`)
    - Produces more human readable code
    - Not as stable as base, breaking changes occur as `tidyverse` develops.
    - Good for interactive data analyst
* Core base <svg style="height:0.8em;top:.04em;position:relative;fill:steelblue;" viewBox="0 0 581 512"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg>
    - Good for production level code
    - Stable
    - Function syntax inconsistent

---

## What is <a href="https://rstudio.com/products/rstudio/"><img src=https://rstudio.com/wp-content/uploads/2018/10/RStudio-Logo-flat.svg width="100px" /></a>?

* RStudio is an integrated development environment (IDE) for <svg style="height:50;fill:bluesteel;" viewBox="0 0 581 512"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg>
* Think of it as the front-end, like a powerful text editor
* R needs to be installed first before RStudio.

---

## <svg style="height:0.8em;top:.04em;position:relative;fill:steelblue;" viewBox="0 0 581 512"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg> Session

* __Working Directory__: Each session runs from a working directory
    - `getwd()` shows the current working directory for R.
    - `setwd(<path>)` to change the working directory where `<path>` is a string
        - E.g. `setwd("C:/Users/usyd-student")` for a windows user.
* __Workspace__: Includes
    - Global environment: Data and variables loaded or defined
    - package environments: Any loaded packages with their functions/data.
* __History__:
    - Can view your recent commands in the History pane
    - Can navigate previous commands using up and down arrows at your prompt
        
---

## Packages

* Inspect the current environment
    - `sessionInfo()`: shows everything in the current R session.
* To install a new package, 
    - `install.packages("cluster")` will install the `cluster` package from the Comprehensive R Archive Network (CRAN) repository
* To load a package to use in the environment `library(cluster)`
    - After loading a package, you will be able to use the functions provided in that package
* If name conflicts arise you can specify the desired function using `::` 
    - E.g. `dplyr::filter` will use the `filter` function from the `dplyr` package instead of the `stats::filter` function.

---

## Help

* CRAN requires every exported function in every contributed package to have a help file
    - `help.start()`
    - `help(plot)`
    - `? plot`
* In general, the help file for each function gives a brief description of what the function does, the required inputs, the expected outputs and some examples
    - Some packages also include a "vignette", a short document with guidance on using the package.

---

## Quirks of R syntax

* `<-` is the symbol for ‘assign’
    - Example: `x <- 14`
    - which is equivalent to: `x = 14` when used at the prompt
* Should use `=` for argument matching in a function
* The period symbol `.` can be used in variable names
    - Example: `new.vector <- c("A", "B", "C")`
* Element indexing starts at 1
    
    ```r
    new.vector <- c("A", "B", "C")
    new.vector[0]
    ```
    
    ```
    ## character(0)
    ```
    
    ```r
    new.vector[1]
    ```
    
    ```
    ## [1] "A"
    ```

---

## Basic data types in R

.pull-left[
Classical data types
* Numeric
* Integer
* Logical
* Character
* Complex
]

.pull-right[

* Factor : Categorical data type
    -  Unique to R (integer with some attributes)

```r
data("ToothGrowth")
levels(ToothGrowth$supp)
```

```
## [1] "OJ" "VC"
```

```r
class(ToothGrowth$supp)
```

```
## [1] "factor"
```

```r
str(ToothGrowth$supp)
```

```
##  Factor w/ 2 levels "OJ","VC": 2 2 2 2 2 2 2 2 2 2 ...
```
]

---

## Homogeneous vs non-homogenous data types in R

### Homogeneous

* Vector
    - Sequence of data elements of the same basic data type
* Matrix 
    - Collection of data elements in a 2-dimensional array with rows and columns

### Non-homogeneous

* List
    - More general structure containing other objects (including possibly other lists)
* Data frame
    - Used for storing data, each column can be a different basic type
    - All columns must have the same length

---

## Vectors

```r
new.vector <- c(1, 2, 3)
class(new.vector)
```

```
## [1] "numeric"
```

```r
length(new.vector)
```

```
## [1] 3
```

```r
new.vector[1:2]
```

```
## [1] 1 2
```

```r
new.vector <- c(1, 2, "hello")
class(new.vector)
```

```
## [1] "character"
```

---

## Matrix

```r
A <- matrix(c(2, 4, 3, 1, 7, 8), nrow = 3)
# Unless specified otherwise, it will fill the matrix by column.
A
```

```
##      [,1] [,2]
## [1,]    2    1
## [2,]    4    7
## [3,]    3    8
```

```r
A[2, 1]
```

```
## [1] 4
```

```r
A[1, ]
```

```
## [1] 2 1
```

```r
A[5]
```

```
## [1] 7
```

---

## List

```r
vector.a <- c(1, 2, 3)
vector.b <- c("hello", "world", "!!")
new.list <- list(c(vector.a, vector.b))
new.list
```

```
## [[1]]
## [1] "1"     "2"     "3"     "hello" "world" "!!"
```

```r
new.list <- list(vector.a, vector.b)
new.list
```

```
## [[1]]
## [1] 1 2 3
## 
## [[2]]
## [1] "hello" "world" "!!"
```

```r
new.list[[1]]
```

```
## [1] 1 2 3
```

---

## Data frames

.pull-left[

```r
head(warpbreaks)
```

```
##   breaks wool tension
## 1     26    A       L
## 2     30    A       L
## 3     54    A       L
## 4     25    A       L
## 5     70    A       L
## 6     52    A       L
```

```r
class(warpbreaks)
```

```
## [1] "data.frame"
```

```r
head(warpbreaks$wool)
```

```
## [1] A A A A A A
## Levels: A B
```
]
.pull-right[

```r
str(warpbreaks)
```

```
## 'data.frame':	54 obs. of  3 variables:
##  $ breaks : num  26 30 54 25 70 52 51 26 67 18 ...
##  $ wool   : Factor w/ 2 levels "A","B": 1 1 1 1 1 1 1 1 1 1 ...
##  $ tension: Factor w/ 3 levels "L","M","H": 1 1 1 1 1 1 1 1 1 2 ...
```

```r
names(warpbreaks)
```

```
## [1] "breaks"  "wool"    "tension"
```
]

---

## Rprojects

* Each Rstudio project has its own working R session, workspace, history, and source documents
* You can either create an Rproject in a new directory or within and existing directory
* When you open an .Rproj file, the following happens:
    - A new R session (process) is started
    - If it exists, the .Rprofile in the project's main directory is loaded
    - If applicable, the .RData file in the main directory is loaded
    - If set, the .Rhistory file in the main directory is loaded
    - The current working directory is set to the project directory
    - If set, previously edited documents appear in the source editor.